CSC 446 Natural Language Processing (NLP)

This course offers a comprehensive introduction to Natural Language Processing (NLP), an essential subfield of Artificial Intelligence that empowers machines to interpret, generate, and act upon human language. Students will study fundamental theories - from text preprocessing and tokenization to syntactic parsing and semantic analysis. Topics include word embeddings, attention mechanisms, transformer architectures (e.g., BERT, GPT), sequence-to-sequence models for machine translation, and language modeling techniques. The curriculum emphasizes classical, rule-based NLP methods and state-of-the-art neural networks, providing students with a historical perspective and cutting-edge techniques. Through hands-on projects and assignments, participants will build practical skills in developing and evaluating real-world NLP applications such as sentiment analysis, chatbots, speech recognition, text summarization, and information extraction. Students will also engage with ethical considerations, including bias in language models and responsible AI deployment, as part of broader discussions on societal impact and fairness. By the end of the course, learners will have developed robust programming proficiency in Python-based NLP frameworks and cultivated the research and analytical skills necessary to tackle emerging challenges in the field. Prerequisites: Basic programming (Python and/or other programming languages), linear algebra, probability, and machine learning fundamentals.

Cross Listed Courses

AI/CSC/DA 446 & 546